Adapting, implementing and evaluating a navigation intervention for older people with cancer and their family caregivers in six countries in Europe: the Horizon Europe-funded EU NAVIGATE project
Bibliographic record
Abstract
Background: Navigation interventions could support, educate and empower older people with cancer and/or their family caregivers by addressing barriers and ensuring timely access to needed services and resources throughout the continuum of supportive, palliative and end-of-life care. Objectives: European Union (EU) NAVIGATE is an interdisciplinary and cross-country Horizon Europe-funded project (2022-2027) aiming to evaluate the effectiveness, cost-effectiveness and implementation of a navigation intervention for older people with cancer and their family caregivers in Europe. EU NAVIGATE aims to advance the evidence on cancer patient navigation in Europe. Design: Adaptation, implementation and evaluation of a navigation intervention with an international pragmatic randomized controlled trial (RCT) and embedded mixed-method process evaluation at its core. A logic model guides dissemination and impact-generating strategies. EU NAVIGATE involves six experienced EU academic partners; one EU national cancer league with their affiliated academic partner; three EU dissemination partners; and a Canadian partner. Methods: ) volunteer programme to healthcare contexts in Belgium, Ireland, Italy, the Netherlands, Poland and Portugal following the new ADAPT guidance. Nav-CARE was developed over the past 15 years and supports people with declining health and their families to improve their quality of life and well-being, foster empowerment and facilitate timely and equitable access to healthcare and social services. In EU NAVIGATE, the navigation intervention is being provided by trained and mentored social workers in Poland and by trained and mentored volunteers in the other five countries. Via a pragmatic RCT with process evaluation, we implement and evaluate the navigation intervention to study its impact on older people with cancer and their family caregivers. We also aim to understand its cost-effectiveness, how to optimally implement it in different countries, and its differential effects in patient subgroups. We will also map existing cancer navigation interventions in Europe, the United States and Canada to position EU NAVIGATE within the field of navigation interventions worldwide. Conclusion: EU NAVIGATE aims to deliver high-quality evidence on a navigation intervention for older people with cancer in Europe and to develop practice and policy recommendations for sustainable implementation of navigation interventions in Europe and beyond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".